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Support vector machine with discriminative low-rank embedding

CAAI Transactions on intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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Towards Efficient Intermittent-Propulsion Mode of a Novel Bioinspired Underwater Vehicle

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Li, Sijie Wu, Zhengxing Wang, Jian Feng, Yukai Tan, Min Yu, Junzhi School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing China

In nature, the bottlenose dolphin employs a prevalent intermittent-flapping behavioral strategy to enhance swimming efficiency during long-distance journeys. Taking inspiration from this natural mechanism, a newly-designed bioinspired underwater vehicle (BUV) based on dorsoventral propulsion is developed to investigate the specific intermittent-propulsion mode. First, a BUV prototype is designed, incorporating morphological characteristics resembling those of adult bottlenose dolphins, which features robust sensing capabilities, redundant actuation surfaces, and a passive peduncle joint for mitigation of the head-nodding motion. Second, a dynamic model for assessing the intermittent propulsion's cost of transport and velocity in a balanced perspective is established. In-depth investigations are conducted to analyze the impact of parameters in the intermittent-propulsion mode under varying flapping and gliding drag coefficients, which offer a novel and reasonable explanation for the exceptional duty cycle observed in bottlenose dolphins by biologists. Finally, numerous aquatic experiments are conducted to validate the remarkable energy-saving advantages of the proposed intermittent-propulsion mode of the BUV. These results present a new solution for efficient and sustainable long-distance navigation of BUVs, highlighting their potential for effective marine exploration. IEEE

关键词： Secondary batteries

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Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of Machines in Manufacturing Industry 3

Systematic Literature Review on Industry Revolution 4.0 to P...

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3rd International Conference on artificial intelligence and Smart Energy, ICAIS 2023

作者： Sowmya, P. Ravichandran, Sathish Kumar Rakshitha Department of Robotics and Artificial Intelligence Karnataka Nitte India Christ University School of Engineering and Technology Department of Computer Science Karnataka Bangalore India Department of Artificial Intelligence and Machine Learning Karnataka Nitte India

ISBN: (纸本)9781665462167

Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance. © 2023 IEEE.

关键词： Industry 4.0

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Deep Learning-based Scratch Detection on Semiconductor Package Die

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Journal of Institute of Control, robotics and Systems 2022年第10期28卷 881-887页

作者： Park, Seong-Beom Hwang, Youngbae Department of Industrial Artificial Intelligence Chungbuk National University Korea Republic of Department of Intelligent Robotics and Systems Chungbuk National University Korea Republic of

The purpose of this paper is to evaluate the detection capability of deep learning-based surface scratches in semiconductor package dies and to determine whether deep learning can be employed as an objective inspection tool. To achieve this aim, detection capabilities are compared and evaluated based on a conventional image processing-based appearance inspection algorithm for examining equipment operating in the semiconductor package inspection process. In this study, a ResNet, which is the widely used CNN-based deep learning model, is used. The dataset was generated by collecting images of semiconductor packages being inspected in the semiconductor package appearance inspection process, which were cropped to focus on the parts necessary for the study. Three experiments were conducted for comparison. In the first experiment, the results of each detection using the Hough transform and ResNet-50 were compared. In the second experiment, the comparison was conducted in the same environment as in the first experiment by adding over-examination images. In the third experiment, the Hough transform algorithm and ResNet-50 were further compared by learning the over-examination images. Based on the results, an objective evaluation was conducted to determine whether deep learning could be applied to image processing-based machine inspection facilities. © ICROS 2022.

关键词： Image processing

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CNN-based Machine Vision Classifier using Channel-Wise Fusion of Multiple Illuminations

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Journal of Institute of Control, robotics and Systems 2022年第10期28卷 855-861页

作者： Lee, Hyojoong Hwang, Youngbae Department of Industrial Artificial Intelligence Chungbuk National University Korea Republic of Department of Intelligent Robotics and Systems Chungbuk National University Korea Republic of

In this paper, a method of combining three grayscale images into a color image and applying it to a recent CNN-based classifier is proposed. Each of the three color channels is assigned by three gray-scale images of a target object taken under different lighting conditions using the same optical system. Consequently, a single virtual color image is obtained. By creating a virtual color image of the classified ball, the generated images and the original gray-scale image used for the machine vision inspection are composed as a dataset, and the classification results are compared by learning with ResNet-50. In the case of bump ball pressing/protrusion, the accuracy of defect classification is computed by conducting experiments five times. Through a comparative evaluation, the accuracy of general grayscale image classification was determined as between 80.2% and 95.5% (depending on lighting conditions), and the average accuracy rate was 89.6%. According to the combination method, the accuracy rate of the virtual color image was distributed between 94.2% and 97.3% and the average accuracy rate was 96.1%. Accordingly, we can confirm a comparative advantage and an average improvement effect of 6.5% in terms of stability and accuracy. © ICROS 2022.

关键词： Optical systems

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Enhancing Healthcare: IoT-LoRaWAN System for Stress Level Monitoring

Enhancing Healthcare: IoT-LoRaWAN System for Stress Level Mo...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Hayati, Nur Riki Aldi, Iqbal Widyasmoro Center of Artificial Intelligence Robotics Studies Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Bantul Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Bantul Indonesia

ISBN: (纸本)9798350379839

Indonesia is grappling with a significant mental health issue as the number of suicides continues to rise each year. This phenomenon aligns with the global trend highlighted by the World Health Organization (WHO), which reports approximately 700,000 suicides occurring worldwide annually. Impaired mental health frequently plays a significant role in cases of suicide. Thus, prioritizing mental well-being is crucial in preventing suicide. Experiencing high levels of stress can significantly worsen one's mental well-being. In order to address the issue at hand, we require a creative approach to monitoring stress levels. This research project focuses on designing and building a stress level monitoring system using IoT-LoRa Wantechnology. The system is capable of monitoring patient stress levels in real-time. Based on the test results, it is evident that the system has achieved an impressive average accuracy of 97% in monitoring stress levels over 20 data collection times. We conducted a thorough analysis to evaluate the precision of the data obtained from the sensor by comparing it with a pulse oximeter. © 2024 IEEE.

关键词： Electronic health record

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A comprehensive survey on security issues in vehicle-to-grid networks

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Journal of Control and Decision 2023年第2期10卷 150-159页

作者： Arun Sekar Rajasekaran Maria Azees Fadi Al-Turjman Department of Electronics and Communication Engineering GMR Institute of TechnologyRajamIndia Artificial Intelligence Engineering Department. Research Center for AI and IoTAI and Robotics InstituteNear East UniversityMersin 10Turkey

Vehicle to grid(V2G)is the most hopeful approach to transfer energy as well as information in the bidirectional way.V2G network is formed by electric vehicles which connect with smart metres for information and energy transfer in a wireless *** though many security preserving schemes developed in V2G networks,they were prone to enormous number of security breaches.A countless deal of works has been done towards it,but security mechanisms in V2G networks are not *** survey work provides a summary about the V2G network characteristics,significance,security services and the security ***,this work offers a summary of some foremost security attacks on various security services such as accessibility,confidentiality,authentication,integrity and non-repudiation and the related countermeasures to make the V2G communications more protected.

关键词： Authentication confidentiality integrity privacy security

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A Framework for Pothole Detection via the AI-Blockchain Integration

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data Engineering and Communications Technologies 2022年 129卷 398-406页

作者： Mubarak, Auwalu Saleh Ameen, Zubaida Said Tonga, Paul Altrjman, Chadi Al-Turjman, Fadi Artificial Intelligence Engineering Department Research Center for AI and IoT AI and Robotics Institute Near East University Mersin 10 Turkey

Potholes are a major cause of road accidents. In smart cities, developing a system that will help in detecting the potholes in real-time while warning the motorist before reaching that location is very important. Lightweight EfficientDet family models were proposed in the literature to detect potholes in real-time. The work is divided into two parts, first a mobile application was developed using these trained models and it can efficiently detect the potholes. Also, in the second part, a blockchain was proposed to keep a record of the location of the pothole in real-time so that it can be updated on the network. The blockchain serves as a security mechanism so that the potholes coordinates can be altered and become up to date. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Human Robot Interaction: Identifying Resembling Emotions Using Dynamic Body Gestures of Robot 3

Human Robot Interaction: Identifying Resembling Emotions Usi...

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3rd IEEE International Conference on artificial intelligence, ICAI 2023

作者： Ali, Sara Mehmood, Faisal Iqbal, Khawaja Fahad Ayaz, Yasar Sajid, Muhammad Sial, Muhammad Baber Malik, Muhammad Faiq Javed, Kashif Department of Robotics and Artificial Intelligence Islamabad Pakistan National University of Sciences and Technology H-12 Department of Robotics and Artificial Intelligence Islamabad Pakistan National University of Sciences and Technology H-12 Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350322125

Natural social interaction between a human and robot requires perception ability of robot for complex social behaviors and in turn displaying the learnt emotional behavior during interaction. The emotions expressed with body language are one of the key parameters during Human-Robot Interaction (HRI). This research focuses on extending the concept of affective non-verbal human-robot interaction using full body gestures for closely resembling emotion in absence of intricate facial expressions. The movements of head, torso, legs, and arms are used to link body movements and gestures of a bi-pedal humanoid robot with 28 closely resembling emotions. Variation of speed, frequency, and joint angles of the robot are the key features used for expressing a specific emotion. This research uses Russell's circumplex model to define 8 primary emotional categories which are further grouped into closely associated emotions resembling each other. 33 participants were involved in experimentation to authenticate different emotions using body gestures of robot. Each participant performed 28 trials in which emotions were displayed at random to recognize the state of robot. The results showed an average accuracy of 79.69%. The study authenticates that perceived emotions are highly dependent on body movements, postures, and selected features. This design model can therefore be used for even conveying the closely resembling emotions. © 2023 IEEE.

关键词： Anthropomorphic robots

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Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第10期5卷 4893-4904页

作者： Liu, Yexin Zhang, Weiming Zhao, Guoyang Zhu, Jinjing Vasilakos, Athanasios V. Wang, Lin Robotics and Autonomous Systems Thrust Guangzhou511458 China Grimstad4879 Norway Artificial Intelligence Thrust Guangzhou511458 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for color-thermal semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of color images is larger than that of thermal images;and 2) the classwise performance of color images at night is not consistently higher or lower than that of thermal images. We propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime color-thermal semantic segmentation without access to the source (daytime) data during adaptation. Our method enjoys three key technical parts. First, as one modality (e.g., color) suffers from a larger domain gap than that of the other (e.g., thermal), imaging heterogeneity refinement (IHR) employs an interaction branch on the basis of color and thermal branches to prevent cross-modal discrepancy and performance degradation. Then, class aware refinement (CAR) is introduced to obtain reliable ensemble logits based on pixel-level distribution aggregation of the three branches. In addition, we also design a specific learning scheme for our TTA framework, which enables the ensemble logits and three student logits to collaboratively learn to improve the quality of predictions during the testing phase of our Night-TTA. Extensive experiments show that our method achieves state-of-the-art (SoTA) performance with a 13.07% boost in mean intersection over union (mIoU). © 2020 IEEE.

关键词： Color

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